1329 
IS05725-2 STANDARD APPLICATION TO VERIFICATION OF ORTHOPHOTO-BASED 
IMPERVIOUS SURFACE AREA AND IMPERVIOUSNESS FACTOR DETERMINATION 
B. Hejmanowska, W. Drzewiecki, A. Wrobel 
Dept, of Geoinformation, Photogrammetry and Remote Sensing of Environment, AGH University of Science and 
Technology, Krakow, Poland - (galia, drzewiec, awrobel)@agh.edu.pl 
KEY WORDS: Imperviousness Factor, Photointerpretation, Accuracy, IKONOS, Airborne Ortophotomap 
ABSTRACT: 
The main aim of the research described in the paper was analyse the accuracy of photointerpretation of impervious surface using 
IKONOS images and its influence on the imperviousness factor determination. Two kinds of IKONOS image were chosen to tests: 
panchromatic and colour pansharp. Airborne ortopho (pixel size of 0.2m) was applied as a reference. Six operators, digitised three 
times, two kinds of IKONOS image, on the six test areas (300x300m). Accuracy analysis was performed applying different 
parameters, among others: RMS and reproducibility (ISO 5725-2). Then, each test area was grided with 30m pixel size (simulation 
Landsat image) and imperviousness factor was in each pixel determined. Mean error for PAN image was ca. 20% and for RGB 
image ca. 10%. 
1. INTRODUCTION 
In many cases traditional land-use / land cover map created 
through classification of satellite images does not provide us 
with information necessary for evaluation of changes occurring 
in the landscape. The process of landscape urbanization can be 
given as an example. In this case the changes may be twofold. 
What can change is not only the type of land-use (eg. from 
agriculture to residential area), but also the level of urbanisation 
within the same land-use type. For former the traditional land- 
use map is enough, for latter may not. Because what changes 
here it is not the land-use type, but the proportions of different 
kinds of land cover inside the same land-use class. Very 
detailed land-use / land cover map made from high resolution 
satellite images or air photos could be proposed in such a case, 
but such a map is very laborious and expensive when large area 
is taken into consideration. Moreover, this kind of images may 
be not available for past years. Continuum-based classification 
of medium-resolution satellite images may be seen as a viable 
alternative (see e.g. Clapham 2003, Xian and Crane 2005, Xian 
2006). As a result of such classification a map of 
imperviousness factor is obtained. The imperviousness factor 
can be defined as a percentage of the area (e.g. percentage of 
the image pixel) covered by impervious surfaces (such as roofs, 
asphalt roads, parking lots, etc.). 
Medium resolution satellite images have been used for the 
assessment of the ground surface imperviousness from 1970s 
(see Jackson 1975). Initially the methodology was based on 
supervised or unsupervised image classification techniques, but 
because of the resolution of these images the results were often 
not satisfactory. Then many new approaches have been 
developed, including among others artificial neural network, 
spectral mixture analysis or regression tree approach. A review 
of up-to-date techniques can be found e.g. in Weng (2008). The 
accuracy of the imperviousness factor estimation reported in 
different studies is usually better then 20 per cent. 
Regardless the approach applied, the information about the 
impervious surfaces acquired in the field or from higher 
resolution data is needed as a training (or calibration) data and 
also for accuracy assessment. The field data are rarely available 
and in the most cases such information is acquired from digital 
aerial or satellite orthophotos. High resolution satellite images 
are commonly used for this purpose. Here we comes to the 
question about the accuracy of these training and more 
importantly validation (or control) datasets. In many cases high 
resolution satellite orthoimages are used. Despite our efforts we 
weren’t able to find in literature any assessments of accuracy 
for imperviousness factor estimations based on photo 
interpretation of high resolution satellite imagery. Actually the 
only information about the accuracy of photo interpretation 
based imperviousness data was find in Deguchi and Sugio 
(1994). They use the aerial photographs in different scales 
(from 1:10000 to 1:23000) to obtain the reference dataset. They 
report the accuracy of the estimation of imperviousness factor 
by visual interpretation of these photographs to be about 10 per 
cent. We could expect similar or even worse accuracy of 
imperviousness factor derived by visual interpretation of high 
resolution satellite imagery. The verification of this assumption 
was set as a goal in research presented in our paper. 
2. ISO 5725-2 STANDARD AND ITS APPLICATION TO 
PHOTO INTERPRETATION 
Acquisition of spatial data should be accompanied by 
acquisition of information about their quality. In our opinion 
information about GIS data accuracy should be seen as one of 
the most important metadata, especially if the data are to be 
used in financial context as penalty (e.g. Integrated 
Administration Control System - IACS, in agriculture financial 
subsidies in EU) or taxation (e.g. cadastre, sewer waters). A 
necessity for such information is also stressed in official 
regulations. In the Directive of European Council from 14 
March 2007 establishing the Infrastructure for Spatial 
Information in the European Community (INSPIRE) we can 
find the following statement: “metadata in spatial database shall 
include information on the quality and validity of spatial data 
sets” (Chapter II, Metadata, Article 5, p.2 c). GIS data metadata 
as defined in ISO 19113 standard contain among others: quality, 
spatial accuracy, temporal accuracy and thematic accuracy.